Ethical Considerations in AI & Data Science for Healthcare
Course Overview
This course explores the ethical implications of AI and data science in healthcare. Participants will learn about bias in AI models, patient privacy concerns, regulatory compliance, and responsible AI practices. Case studies will highlight real-world ethical dilemmas and solutions. By the end, students will be equipped to navigate ethical challenges while developing and deploying AI solutions in healthcare.
Key Skills
- Supervised Learning Fundamentals (Classification & Regression)
- Python for Machine Learning (Pandas, NumPy, Scikit-learn)
- Key ML Algorithms (Linear Regression, Decision Trees, SVM, k-NN)
- Model Evaluation & Metrics (Accuracy, Precision, Recall, F1-Score)
- Data Preprocessing & Feature Engineering
- Hyperparameter Tuning & Model Optimization
Course Outline
Introduction to Supervised Machine Learning in Python
Machine Learning Concepts
Lessons Objective
- Understanding Supervised Learning (Classification & Regression)
- Model Training & Evaluation
- Overfitting & Underfitting
Python & ML Libraries
Lessons Objective
- Working with Scikit-learn
- Data Handling with Pandas & NumPy
- Data Visualization using Matplotlib & Seaborn
Supervised Learning Algorithms
Lessons Objective
- Linear Regression
- Decision Trees
- Support Vector Machines (SVM)
- k-Nearest Neighbors (k-NN)
- Logistic Regression
Model Evaluation & Optimization
Lessons Objective
- Performance Metrics (Accuracy, Precision, Recall, F1-Score)
- Train-Test Split & Cross-Validation
- Hyperparameter Tuning
- Feature Selection & Engineering
Projects in this course
In this project, you will apply supervised machine learning techniques to predict customer churn for a telecom company. Using a real-world dataset, you will:
- Preprocess the data (handling missing values, encoding categorical features)
- Train and evaluate models like Logistic Regression, Decision Trees, and k-NN
- Compare model performance using metrics like accuracy, precision, recall, and F1-score
- Optimize models through hyperparameter tuning
- Visualize insights with Matplotlib & Seaborn
By completing this project, you will gain hands-on experience in classification problems, model evaluation, and real-world data handling.
Course Duration:
10 Hours
Earned Skills:
Python, Problem Solving, Supervised Learning Algorithms
Earn Certification:
Earned a valuable certificate to boost your resume
